diff --git a/LICENSE.md b/LICENSE.md
index 54b0725f..7ec78493 100644
--- a/LICENSE.md
+++ b/LICENSE.md
@@ -1,5 +1,3 @@
-The MIT License (MIT)
-
 Copyright (c) 2016 Marcin Junczys-Dowmunt
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
@@ -9,8 +7,8 @@ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 copies of the Software, and to permit persons to whom the Software is
 furnished to do so, subject to the following conditions:
 
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
 
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
diff --git a/src/expression_graph.cu b/src/expression_graph.cu
index 52a68893..fbdaa084 100644
--- a/src/expression_graph.cu
+++ b/src/expression_graph.cu
@@ -1,8 +1,6 @@
 #include <sstream>
 #include "expression_graph.h"
 
-using namespace std;
-
 namespace marian {
 
 Expr::Expr(ExpressionGraphPtr g, Chainable<Tensor>* chainable)
@@ -32,19 +30,10 @@ Expr::operator ChainPtr() {
 
 std::string Expr::Debug() const
 {
-	stringstream strm;
+	std::stringstream strm;
 	const Shape &shape = pimpl_->shape();
 	strm << marian::Debug(shape);
 	return strm.str();
 }
 
-///////////////////////////////////////////////////////
-//ExpressionGraph::ExpressionGraph(int cudaDevice)
-//: stack_(new ChainableStack)
-//{
-//  std::srand (time(NULL));
-//  cudaSetDevice(0);
-//
-//}
-
 }
diff --git a/src/sgd.h b/src/sgd.h
index 7d3f3200..85a4e4af 100644
--- a/src/sgd.h
+++ b/src/sgd.h
@@ -26,16 +26,17 @@ class Adagrad {
     Adagrad(float eta=0.1) : eta_(eta) {}
     
     void operator()(ExpressionGraph& graph, int batchSize) {
+      float fudgeFactor = 1e-6;
+      graph.backprop(batchSize);
+      
       if(history_.size() < graph.params().size())
         for(auto& param : graph.params())
           history_.emplace_back(Tensor(param.grad().shape(), 0));
       
-      graph.backprop(batchSize);
-      
       auto it = history_.begin();
       for(auto& param : graph.params()) {    
-        Element(_1 -= eta_ / Sqrt(_2) * _3, param.val(), *it, param.grad());
         Element(_1 += _2 * _2, *it, param.grad());
+        Element(_1 -= eta_ / (fudgeFactor + Sqrt(_2)) * _3, param.val(), *it, param.grad());
         it++;
       }
     }